referral
Referral systems - referral programs, viral loops. Use for referrals.
When & Why to Use This Skill
This Claude skill provides a comprehensive framework for designing, implementing, and optimizing referral systems and viral growth loops. It focuses on building high-integrity growth engines with a focus on accurate attribution, fraud prevention, and data-driven optimization using a modern tech stack including PostHog, Neon, and Drizzle.
Use Cases
- Designing fraud-resistant referral programs that prevent duplicate abuse and self-referrals to protect marketing budgets.
- Implementing accurate, auditable referral attribution systems to ensure users are rewarded correctly across all channels.
- Analyzing viral loop performance and referral conversion rates using integrated PostHog analytics to identify growth bottlenecks.
- Architecting scalable database schemas for managing complex referral rewards and user-to-user relationship tracking.
- Developing clear terms and automated enforcement mechanisms for referral programs to maintain user trust.
| name | referral |
|---|---|
| description | Referral systems - referral programs, viral loops. Use for referrals. |
Referral Guideline
Tech Stack
- Analytics: PostHog
- Database: Neon (Postgres)
- ORM: Drizzle
Non-Negotiables
- Referral attribution must be accurate and auditable
- Rewards must be fraud-resistant (no self-referral, duplicate abuse)
- Terms must be clear and enforced consistently
Context
Referrals turn happy users into growth engines. But poorly designed referral programs create fraud opportunities and erode trust. The best referral programs feel generous, not gameable.
Driving Questions
- What would make users genuinely want to refer others?
- How do we prevent referral fraud without punishing legitimate users?
- Is referral attribution working correctly across all channels?
- What's the referral conversion rate and what affects it?
- Are referral rewards actually motivating behavior?